• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.963-972
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• 2009

The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.

• Korean Journal of Construction Engineering and Management
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• v.8 no.3
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• pp.76-86
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• 2007

Construction development work invents profit as those finalize object, and a make or break success of project depends on correct analysis and forecast business feasibility at project early. Business feasibility study would be decision-making under precarious situation because is connoting uncertainty that is future. estimate at present visual point essentially. Under uncertainty, a decision-making method is based on probability theory of statistics, but business feasibility study had applied with not feasibility study by probabilistic decision method but it by determinism derision method so far. Therefore in this study doing decision-making by a probability theory method for successful project at early business feasibility study, it present a probabilistic study method that use simulation that can supply a little more correct and reliable data to decision-maker As result, a probabilistic study method is more suitable than deterministic study method as technique for a financial feasibility study of construction development work. Making good use of this probabilistic study method at important business or careful decision-making, because efficient Judgment that is based accuracy and authoritativeness may become available.

• Journal of Korea Water Resources Association
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• v.35 no.6
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• pp.817-826
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• 2002

Many researchers have been tried to forecast the future as analyzing data characteristics and the forecasting methodology may be divided into two cases of deterministic and stochastic techniques. However, the understanding data characteristics may be very important for model construction and forecasting. In the sense of this view, recently, the deterministic method known as nonlinear dynamics has been studied in many fields. This study uses the geometrical methodology suggested by Poincare for analyzing nonlinear dynamic systems and we apply the methodology to understand the characteristics of known systems and hydrologic data, and determines the possibility of forecasting according to the data characteristics. Say, we try to understand the data characteristics as constructing Poincare map by using Poincare section and could conjecture that the data sets are linear or nonlinear and an appropriate model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.341-350
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• 2000

The objectives of this study are to develop a state-space form of stochastic dynamic storage function routing model and to test the model performance for real-time flow forecast. The selected study area is the main Han River starting from Paldang Dam site to Indogyo station and the 13 flood events occurred from 1987 to 1998 are selected for computing model parameters and testing the model performance. It was shown that the optimal model parameters are quite different depending on Hood events, but the values used on field work also give reasonable results in this study area. It is also obvious that the model performance from the stochastic-dynamic model developed in this study gives more accurate and reliable results than that from the existing deterministic model. Analysis for allowable forecast lead time leads that under the current time step the reasonable predicted downstream flows in 5 hours time advance are obtained from the stochastic dynamic model on relatively less lateral inflow event in the study area.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.953-961
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• 2009

The objective of this study is to develop a stochastic continuous storage function model for enhancement of an event-oriented watershed and channel storage function models which have been used as an official flood forecast model in Korea. For this study, soil moisture accounting component is added to the original storage function model and each hydrologic component, such as surface flow, subsurface flow, groundwater flow and actual evaportranspiration, is simulated as a function of soil water content. And also, ensemble Kalman filtering technique is used for real-time assimilation of measured streamflow from various stream locations in the watershed. Therefore the enhanced model will be able to simulate hydrologic components for long-term period without additional estimation of model parameters and to give more accurate and reliable results than those from the existing deterministic model due to the assimilation of measured streamflow data.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.411-418
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• 1992

The purpose of this study is to develop dynamic-stochastic models that can forecast the inflow into reservoir during low/drought periods and flood periods. For the formulation of the models, the discrete transfer function is utilized to construct the deterministic characteristics, and the ARIMA model is utilized to construct the stochastic characteristics of residuals. The stochastic variations and structures of time series on hydrological data are examined by employing the auto/cross covariance function and auto/cross correlation function. Also, general modeling processes and forecasting method are used the model building methods of Box and Jenkins. For the verifications and applications of the developed models, the Chungju multi-purpose reservoir which is located in the South Han river systems is selected. Input data required are the current and past reservoir inflow and Yungchun water levels. In order to transform the water level at Yungchon into streamflows, the water level-streamflows rating curves at low/drought periods and flood periods are estimated. The models are calibrated with the flood periods of 1988 and 1989 and hourly data for 1990 flood are analyzed. Also, for the low/drought periods, daily data of 1988 and 1989 are calibrated, and daily data for 1989 are analyzed.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.383-386
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• 2001

The ability to make good cost overruns predictions is a very important aspect of in major construction project. The probabilistic cost models can provide more reliable than traditional cost models which have been used in korea to prepare Bill of Quantities, if the actual cost data are sufficient enough to analyze the trends of the variables. The paper considers non-deterministic methods in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The objectives of this research is to develop a method to forecast the probabilistic total construction cost and the elemental work cost

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.